A process-oriented probabilistic linguistic decision-making model with unknown attribute weights

As an effective tool to describe qualitative evaluations, probabilistic linguistic term set (PLTS) can identify the different preference degrees for the possible linguistic evaluations. For the multi-attribute decision making (MADM) problems based on the PLTSs, making decisions is not instantaneous behavior but needs some time to complete information processing. Considering the dynamic nature of decision-making behavior, this study aims to develop a process-oriented probabilistic linguistic decision-making framework. First, we introduce the parameters in the probabilistic linguistic multi-alternative decision field theory (PLMDFT) model. An improved decision rule for selecting the optimal alternative(s) is also presented. Then, a deviation entropy-based model is developed to determine attribute weights. Furthermore, we construct a probabilistic linguistic decision-making framework based on the PLMDFT and deviation entropy. Finally, the constructed framework is applied to solve emergency scheme selection problem. Some discussion and comparative analysis is complemented to demonstrate the validity of the proposed framework.